Document Type
Article
Publication Date
6-12-2021
Department
Department of Computer Science; Department of Geological and Mining Engineering and Sciences
Abstract
Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.
Publication Title
Remote Sensing
Recommended Citation
Ewing, J.,
Oommen, T.,
Jayakumar, P.,
&
Alger, R.
(2021).
Characterizing soil stiffness using thermal remote sensing and machine learning.
Remote Sensing,
13(12).
http://doi.org/10.3390/rs13122306
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15119
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/rs13122306